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"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?
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AI has surpassed humans across a variety of tasks such as image classification, playing games (e.g., go, "Starcraft" and poker), and protein structure prediction. However, at the same time, AI is also bearing serious controversies. Many researchers argue that little substantial progress has been made for AI in recent decades. In this paper, the author (1) explains why controversies about AI exist; (2) discriminates two paradigms of AI research, termed "weak AI" and "strong AI" (a.k.a. artificial general intelligence); (3) clarifies how to judge which paradigm a research work should be classified into; (4) discusses what is the greatest value of "weak AI" if it has no chance to develop into "strong AI".
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Learning in Deep Networks under Dale's Constraint
An on-off two-channel network with fixed-sign synapses and local Hebbian learning is claimed to recover backpropagation exactly under symmetric weights and to beat comparable vanilla networks on Tiny ImageNet.
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